6.2.1. Implement Azure Event Hubs

💡 First Principle: The fundamental purpose of Azure Event Hubs is to provide a highly scalable, durable, and partitioned event ingestion service, enabling the decoupling of event producers from consumers for massive-scale, real-time data streaming.

Scenario: You need to collect streaming telemetry data from hundreds of thousands of sensors. This data arrives continuously and needs to be ingested at a very high rate, processed by multiple independent backend services (each with its own processing logic), and also archived for long-term analysis.

What It Is: Azure Event Hubs is a fully managed, real-time data streaming and event ingestion platform designed for high-throughput scenarios. It can receive and process millions of events per second, making it ideal for big data analytics and telemetry.

Core Components:

• Event Producers: Applications, services, or devices that send event data to an Event Hub using supported protocols (AMQP, HTTPS, SDKs).
• Event Consumers: Applications or services that read and process events from Event Hubs, enabling downstream analytics or actions.
• Consumer Groups: Logical views that allow multiple independent applications to read the same event stream at their own pace, each maintaining its own read position.
• Partitions: Event Hubs are divided into partitions, enabling parallel event processing and high throughput. Events are distributed across partitions, either round-robin or by partition key.

Key Features:

• Data Retention: Events are retained for a configurable period (up to 7 days by default, or up to 90 days for Event Hubs Premium/Dedicated), allowing consumers to process data asynchronously or reprocess historical data.
• Capture: Automatically store streaming data in Azure Blob Storage or Azure Data Lake Storage for batch processing or archival, without needing a separate consumer.
• Integration: Seamless integration with Azure Stream Analytics, Apache Spark, and other analytics engines for real-time or batch processing.

Common Use Cases:

• Real-time telemetry ingestion from IoT devices.
• Clickstream analytics for web/mobile applications.
• Centralized log and event aggregation from distributed systems.
• Security auditing and anomaly detection.

⚠️ Common Pitfall: Using a single consumer group for multiple, distinct processing applications. This causes the applications to compete for events and interfere with each other's progress. Each distinct application should have its own consumer group.

Key Trade-Offs:

• Number of Partitions vs. Throughput/Complexity: More partitions allow for higher throughput and parallelism but can add complexity to the consumer logic, especially if ordering is required within a subset of data.

Practical Implementation: Sending an Event (C#)

using Azure.Messaging.EventHubs;
using Azure.Messaging.EventHubs.Producer;
using System.Text;

var connectionString = "<YOUR_CONNECTION_STRING>";
var eventHubName = "<YOUR_EVENT_HUB_NAME>";

await using (var producerClient = new EventHubProducerClient(connectionString, eventHubName))
{
    using EventDataBatch eventBatch = await producerClient.CreateBatchAsync();
    eventBatch.TryAdd(new EventData(Encoding.UTF8.GetBytes("First event")));
    eventBatch.TryAdd(new EventData(Encoding.UTF8.GetBytes("Second event")));

    await producerClient.SendAsync(eventBatch);
    Console.WriteLine("A batch of 2 events has been published.");
}

Reflection Question: How does implementing Azure Event Hubs, with its partitions, consumer groups, and Capture feature, fundamentally enable scalable, decoupled architectures for ingesting and processing large volumes of real-time event data, supporting both real-time and batch analytics?